precision prevention of Gastric Cancer: A Novel Risk Stratification Strategy Integrating Clinicopathological Features and IGFBp7

precision prevention of Gastric Cancer: A Novel Risk Stratification Strategy Integrating Clinicopathological Features and IGFBp7 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article precision prevention of Gastric Cancer: A Novel Risk Stratification Strategy Integrating Clinicopathological Features and IGFBp7 Xiaohui Guo, Xiaoli Xie, Yujian He, Yuqi Liu, Xiaoxu Jin, Yanyan Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8531917/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Background Chronic atrophic gastritis (CAG) is a critical precursor lesion of gastric cancer (GC); however, precise tools for identifying high-risk individuals are currently lacking. This study aimed to develop a predictive model integrating clinicopathological characteristics and molecular biomarkers to enable individualized risk assessment for GC progression in CAG patients. Methods Gene expression profiles from GEO were analyzed using differential expression genes (DEGs) analysis,weighted gene co-expression network analysis (WGCNA). Random Forest and Support Vector Machine assessed diagnostic gene.This retrospective study enrolled 153 CAG patients (34 of whom progressed to early GC or GC).The least absolute shrinkage and selection operator (LASSO) regression analysis were utilized to identify risk factors for CAG. Based on the factors, a nomogram was constructed. Immunohistochemistry (IHC) was employed to validate the protein expression level of the key biomarker, which was subsequently integrated into the clinical model to create a novel combined model. Results Age, smoking history, and the degree of gastric mucosal atrophy were identified as independent risk factors for GC progression. The clinical nomogram based on these factors demonstrated good predictive capability. Multi-omics analysis revealed that IGFBp7 was significantly upregulated in tissues from patients who progressed to GC. Integrating the IHC score of IGFBp7 into the clinical prediction model significantly enhanced its predictive performance. Conclusion We successfully developed and validated a nomogram model combining clinical risk factors and the biomarker IGFBp7. This model effectively identifies CAG patients at high risk for GC, providing a practical tool for implementing precision surveillance and early intervention. Health sciences/Biomarkers Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Health sciences/Gastroenterology Biological sciences/Genetics Health sciences/Oncology Chronic atrophic gastritis Gastric cancer Risk prediction Nomogram Insulin-like growth factor-binding protein 7 (IGFBp7) Machine learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 06 Mar, 2026 Reviews received at journal 05 Mar, 2026 Reviewers agreed at journal 05 Mar, 2026 Reviews received at journal 21 Feb, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviewers agreed at journal 01 Feb, 2026 Reviewers invited by journal 22 Jan, 2026 Editor invited by journal 12 Jan, 2026 Editor assigned by journal 07 Jan, 2026 Submission checks completed at journal 07 Jan, 2026 First submitted to journal 06 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8531917","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":578864585,"identity":"8a6f7a96-8af3-4bae-8128-deeac4a768cf","order_by":0,"name":"Xiaohui Guo","email":"","orcid":"","institution":"Affiliated Hospital of Hebei Engineering University","correspondingAuthor":false,"prefix":"","firstName":"Xiaohui","middleName":"","lastName":"Guo","suffix":""},{"id":578864586,"identity":"e238cf34-3b12-41c3-ac6c-da6f4ea4cd87","order_by":1,"name":"Xiaoli Xie","email":"","orcid":"","institution":"The Second Hospital of Hebei Medical University, Hebei 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IGFBp7","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Chronic atrophic gastritis, Gastric cancer, Risk prediction, Nomogram, Insulin-like growth factor-binding protein 7 (IGFBp7), Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-8531917/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8531917/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChronic atrophic gastritis (CAG) is a critical precursor lesion of gastric cancer (GC); however, precise tools for identifying high-risk individuals are currently lacking. This study aimed to develop a predictive model integrating clinicopathological characteristics and molecular biomarkers to enable individualized risk assessment for GC progression in CAG patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene expression profiles from GEO were analyzed using differential expression genes (DEGs) analysis,weighted gene co-expression network analysis (WGCNA). Random Forest and Support Vector Machine assessed diagnostic gene.This retrospective study enrolled 153 CAG patients (34 of whom progressed to early GC or GC).The least absolute shrinkage and selection operator (LASSO) regression analysis were utilized to identify risk factors for CAG. Based on the factors, a nomogram was constructed. Immunohistochemistry (IHC) was employed to validate the protein expression level of the key biomarker, which was subsequently integrated into the clinical model to create a novel combined model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAge, smoking history, and the degree of gastric mucosal atrophy were identified as independent risk factors for GC progression. The clinical nomogram based on these factors demonstrated good predictive capability. Multi-omics analysis revealed that IGFBp7 was significantly upregulated in tissues from patients who progressed to GC. Integrating the IHC score of IGFBp7 into the clinical prediction model significantly enhanced its predictive performance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe successfully developed and validated a nomogram model combining clinical risk factors and the biomarker IGFBp7. This model effectively identifies CAG patients at high risk for GC, providing a practical tool for implementing precision surveillance and early intervention.\u003c/p\u003e","manuscriptTitle":"precision prevention of Gastric Cancer: A Novel Risk Stratification Strategy Integrating Clinicopathological Features and IGFBp7","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-24 01:59:09","doi":"10.21203/rs.3.rs-8531917/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-06T11:33:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-06T03:03:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"128549195535075075972871212730052564775","date":"2026-03-06T01:15:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-21T06:42:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"279104510547069585847855518831507936572","date":"2026-02-13T02:17:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177034564267034254928747862841130487838","date":"2026-02-02T02:40:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-22T07:58:17+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-12T06:00:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-07T05:54:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-07T05:53:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-01-06T13:20:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f8efa78d-5499-4be0-a56c-8777f52c15ee","owner":[],"postedDate":"January 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":61591073,"name":"Health sciences/Biomarkers"},{"id":61591074,"name":"Biological sciences/Cancer"},{"id":61591075,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":61591076,"name":"Health sciences/Gastroenterology"},{"id":61591077,"name":"Biological sciences/Genetics"},{"id":61591078,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-04-27T16:06:57+00:00","versionOfRecord":{"articleIdentity":"rs-8531917","link":"https://doi.org/10.1038/s41598-026-49722-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-04-24 15:59:49","publishedOnDateReadable":"April 24th, 2026"},"versionCreatedAt":"2026-01-24 01:59:09","video":"","vorDoi":"10.1038/s41598-026-49722-6","vorDoiUrl":"https://doi.org/10.1038/s41598-026-49722-6","workflowStages":[]},"version":"v1","identity":"rs-8531917","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8531917","identity":"rs-8531917","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}